Brazillian Ecommerce - EDA
Evan Canfield
3/15/2020
Import
Libraries
if (!require(pacman)) {install.packages('pacman')} ## Loading required package: pacman## Warning: package 'pacman' was built under R version 3.6.1p_load(
janitor
, lubridate
, Hmisc
, skimr
, tidyverse
)Data
# Olist Dataset
df_cust <- read.csv(file = "data/olist_customers_dataset.csv", stringsAsFactors = FALSE)
df_geo_loc <- read.csv(file = "data/olist_geolocation_dataset.csv", stringsAsFactors = FALSE)
df_order_items <- read.csv(file = "data/olist_order_items_dataset.csv", stringsAsFactors = FALSE)
df_order_pay <- read.csv(file = "data/olist_order_payments_dataset.csv", stringsAsFactors = FALSE)
df_order_review <- read.csv(file = "data/olist_order_reviews_dataset.csv", stringsAsFactors = FALSE)
df_orders <- read.csv(file = "data/olist_orders_dataset.csv", stringsAsFactors = FALSE)
df_products <- read.csv(file = "data/olist_products_dataset.csv", stringsAsFactors = FALSE)
df_sellers <- read.csv(file = "data/olist_sellers_dataset.csv", stringsAsFactors = FALSE)
df_translation <- read.csv(file = "data/product_category_name_translation.csv", stringsAsFactors = FALSE)
# Brazillian Population Data
df_brazil_pop <- read.csv(file = "data/population_estimates_dou_2019.csv", stringsAsFactors = FALSE)Clean Names
The following function converts column names to snake case.
clean_names_func <- function(df){
df <- df %>% clean_names(case = "snake")
return (df)
}
df_cust <- clean_names_func(df_cust)
df_geo_loc <- clean_names_func(df_geo_loc)
df_order_items <- clean_names_func(df_order_items)
df_order_pay <- clean_names_func(df_order_pay)
df_orders <- clean_names_func(df_orders)
df_products <- clean_names_func(df_products)
df_sellers <- clean_names_func(df_sellers)
df_translation <- clean_names_func(df_translation)
df_brazil_pop <- clean_names_func(df_brazil_pop)Inspect Data
df_cust %>% glimpse()## Observations: 99,441
## Variables: 5
## $ customer_id <chr> "06b8999e2fba1a1fbc88172c00ba8bc7", "...
## $ customer_unique_id <chr> "861eff4711a542e4b93843c6dd7febb0", "...
## $ customer_zip_code_prefix <int> 14409, 9790, 1151, 8775, 13056, 89254...
## $ customer_city <chr> "franca", "sao bernardo do campo", "s...
## $ customer_state <chr> "SP", "SP", "SP", "SP", "SP", "SC", "...df_geo_loc %>% glimpse()## Observations: 1,000,163
## Variables: 5
## $ geolocation_zip_code_prefix <int> 1037, 1046, 1046, 1041, 1035, 1012...
## $ geolocation_lat <dbl> -23.54562, -23.54608, -23.54613, -...
## $ geolocation_lng <dbl> -46.63929, -46.64482, -46.64295, -...
## $ geolocation_city <chr> "sao paulo", "sao paulo", "sao pau...
## $ geolocation_state <chr> "SP", "SP", "SP", "SP", "SP", "SP"...df_order_items %>% glimpse()## Observations: 112,650
## Variables: 7
## $ order_id <chr> "00010242fe8c5a6d1ba2dd792cb16214", "00018...
## $ order_item_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ product_id <chr> "4244733e06e7ecb4970a6e2683c13e61", "e5f2d...
## $ seller_id <chr> "48436dade18ac8b2bce089ec2a041202", "dd7dd...
## $ shipping_limit_date <chr> "2017-09-19 09:45:35", "2017-05-03 11:05:1...
## $ price <dbl> 58.90, 239.90, 199.00, 12.99, 199.90, 21.9...
## $ freight_value <dbl> 13.29, 19.93, 17.87, 12.79, 18.14, 12.69, ...df_order_pay %>% glimpse()## Observations: 103,886
## Variables: 5
## $ order_id <chr> "b81ef226f3fe1789b1e8b2acac839d17", "a981...
## $ payment_sequential <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ payment_type <chr> "credit_card", "credit_card", "credit_car...
## $ payment_installments <int> 8, 1, 1, 8, 2, 2, 1, 3, 6, 1, 8, 1, 1, 5,...
## $ payment_value <dbl> 99.33, 24.39, 65.71, 107.78, 128.45, 96.1...df_order_review %>% glimpse()## Observations: 100,000
## Variables: 7
## $ review_id <chr> "7bc2406110b926393aa56f80a40eba40", "8...
## $ order_id <chr> "73fc7af87114b39712e6da79b0a377eb", "a...
## $ review_score <int> 4, 5, 5, 5, 5, 1, 5, 5, 5, 4, 5, 5, 4,...
## $ review_comment_title <chr> "", "", "", "", "", "", "", "", "", "r...
## $ review_comment_message <chr> "", "", "", "Recebi bem antes do prazo...
## $ review_creation_date <chr> "2018-01-18 00:00:00", "2018-03-10 00:...
## $ review_answer_timestamp <chr> "2018-01-18 21:46:59", "2018-03-11 03:...df_orders %>% glimpse()## Observations: 99,441
## Variables: 8
## $ order_id <chr> "e481f51cbdc54678b7cc49136f2d6af...
## $ customer_id <chr> "9ef432eb6251297304e76186b10a928...
## $ order_status <chr> "delivered", "delivered", "deliv...
## $ order_purchase_timestamp <chr> "2017-10-02 10:56:33", "2018-07-...
## $ order_approved_at <chr> "2017-10-02 11:07:15", "2018-07-...
## $ order_delivered_carrier_date <chr> "2017-10-04 19:55:00", "2018-07-...
## $ order_delivered_customer_date <chr> "2017-10-10 21:25:13", "2018-08-...
## $ order_estimated_delivery_date <chr> "2017-10-18 00:00:00", "2018-08-...df_products %>% glimpse()## Observations: 32,951
## Variables: 9
## $ product_id <chr> "1e9e8ef04dbcff4541ed26657ea517e5",...
## $ product_category_name <chr> "perfumaria", "artes", "esporte_laz...
## $ product_name_lenght <int> 40, 44, 46, 27, 37, 60, 56, 56, 57,...
## $ product_description_lenght <int> 287, 276, 250, 261, 402, 745, 1272,...
## $ product_photos_qty <int> 1, 1, 1, 1, 4, 1, 4, 2, 1, 1, 1, 4,...
## $ product_weight_g <int> 225, 1000, 154, 371, 625, 200, 1835...
## $ product_length_cm <int> 16, 30, 18, 26, 20, 38, 70, 40, 27,...
## $ product_height_cm <int> 10, 18, 9, 4, 17, 5, 24, 8, 13, 10,...
## $ product_width_cm <int> 14, 20, 15, 26, 13, 11, 44, 40, 17,...df_sellers %>% glimpse()## Observations: 3,095
## Variables: 4
## $ seller_id <chr> "3442f8959a84dea7ee197c632cb2df15", "d1...
## $ seller_zip_code_prefix <int> 13023, 13844, 20031, 4195, 12914, 20920...
## $ seller_city <chr> "campinas", "mogi guacu", "rio de janei...
## $ seller_state <chr> "SP", "SP", "RJ", "SP", "SP", "RJ", "PE...df_translation %>% glimpse()## Observations: 71
## Variables: 2
## $ i_product_category_name <chr> "beleza_saude", "informatica_ace...
## $ product_category_name_english <chr> "health_beauty", "computers_acce...df_brazil_pop %>% glimpse()## Observations: 5,570
## Variables: 5
## $ uf <chr> "RO", "RO", "RO", "RO", "RO", "RO", "RO", "...
## $ cod_uf <int> 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,...
## $ cod_munic <int> 15, 23, 31, 49, 56, 64, 72, 80, 98, 106, 11...
## $ nome_do_municipio <chr> "Alta Floresta D'Oeste", "Ariquemes", "Cabi...
## $ populacao_estimada <int> 22945, 107863, 5312, 85359, 16323, 15882, 7...Data Processing
There are several areas where we need to perform processing on the data before we can start to analyze.
Translate
We need to translate the prodcut category name from Protugese to English for greater understanding. The dataset provides a translation code dataset
df_products_translate <- df_products %>%
left_join(y = df_translation,
by = c("product_category_name" = "i_product_category_name")) %>%
select(product_id, product_category_name, product_category_name_english, everything()) %>%
select(-product_category_name)Now that we’ve translated the data, we’ll take a deeper look at the contents of the dataframe.
#df_products_translate %>% describe()Product IDs - Shorten
The product ID tags are very long, and difficult to work with. For other id tags, this isn’t a problem, because they will only be used for connecting tables. But the product id may be displayed on charts and tables.
I wasn to see if I can shorten it, while maintaining the unique tag nature. There are 32,951 unique product id tags.
prod_unique <- df_products_translate %>%
select(product_id) %>%
n_distinct()
prod_unique## [1] 32951The shortest product string in the product id column is 32 characters. So is the maximum. In actuality, all product id tags are 32 characters.
min_prod_id_string <- min(nchar(df_products_translate$product_id))
paste0("The minimum length of a product id string is ", min_prod_id_string, " characters.")## [1] "The minimum length of a product id string is 32 characters."If we shorten the tag, we need to determine that we still have unique ids. The following code crops the product id by n characters, from the right. It then checks against the known number of unique product ids. The process continues until enough characters have been cropped to create some duplicate id values.
n <- prod_unique
x = min_prod_id_string
while (TRUE){
if (n == prod_unique){
n <- df_products_translate %>%
select(product_id) %>%
mutate(product_id = str_sub(product_id, start = -x)) %>%
n_distinct()
#print(paste0("Unique values: ",n))
#print(paste0("Number of digits:", x))
#print(paste0("***"))
x = x-1
}
else{
output_response <- paste0("Shortest string to maintain unique product_id: ", x + 2, " characters.")
return(print(output_response))
}
}## [1] "Shortest string to maintain unique product_id: 8 characters."So, we can shorten product id to only 8 characters, a much more manageable string to view in tables and charts. We need to make this change at all product_id instances.
df_products_translate <- df_products_translate %>%
mutate(product_id = str_sub(product_id, start = -x-2))
df_order_items <- df_order_items %>%
mutate(product_id = str_sub(product_id, start = -x-2))Shipping Times
shipping times will be an important variable for further analysis. We need to convert date-time values, which were imported as characters, into Posix date-time values. Once this is complete, we can then easily
Let’s take a quick look at the table.
df_orders %>%
head()## order_id customer_id
## 1 e481f51cbdc54678b7cc49136f2d6af7 9ef432eb6251297304e76186b10a928d
## 2 53cdb2fc8bc7dce0b6741e2150273451 b0830fb4747a6c6d20dea0b8c802d7ef
## 3 47770eb9100c2d0c44946d9cf07ec65d 41ce2a54c0b03bf3443c3d931a367089
## 4 949d5b44dbf5de918fe9c16f97b45f8a f88197465ea7920adcdbec7375364d82
## 5 ad21c59c0840e6cb83a9ceb5573f8159 8ab97904e6daea8866dbdbc4fb7aad2c
## 6 a4591c265e18cb1dcee52889e2d8acc3 503740e9ca751ccdda7ba28e9ab8f608
## order_status order_purchase_timestamp order_approved_at
## 1 delivered 2017-10-02 10:56:33 2017-10-02 11:07:15
## 2 delivered 2018-07-24 20:41:37 2018-07-26 03:24:27
## 3 delivered 2018-08-08 08:38:49 2018-08-08 08:55:23
## 4 delivered 2017-11-18 19:28:06 2017-11-18 19:45:59
## 5 delivered 2018-02-13 21:18:39 2018-02-13 22:20:29
## 6 delivered 2017-07-09 21:57:05 2017-07-09 22:10:13
## order_delivered_carrier_date order_delivered_customer_date
## 1 2017-10-04 19:55:00 2017-10-10 21:25:13
## 2 2018-07-26 14:31:00 2018-08-07 15:27:45
## 3 2018-08-08 13:50:00 2018-08-17 18:06:29
## 4 2017-11-22 13:39:59 2017-12-02 00:28:42
## 5 2018-02-14 19:46:34 2018-02-16 18:17:02
## 6 2017-07-11 14:58:04 2017-07-26 10:57:55
## order_estimated_delivery_date
## 1 2017-10-18 00:00:00
## 2 2018-08-13 00:00:00
## 3 2018-09-04 00:00:00
## 4 2017-12-15 00:00:00
## 5 2018-02-26 00:00:00
## 6 2017-08-01 00:00:00Now lets look for missing data. Lucky for us there is no missing data in this table.
df_orders %>% skim()## Skim summary statistics
## n obs: 99441
## n variables: 8
##
## -- Variable type:character -----------------------------------------------------
## variable missing complete n min max empty
## customer_id 0 99441 99441 32 32 0
## order_approved_at 0 99441 99441 0 19 160
## order_delivered_carrier_date 0 99441 99441 0 19 1783
## order_delivered_customer_date 0 99441 99441 0 19 2965
## order_estimated_delivery_date 0 99441 99441 19 19 0
## order_id 0 99441 99441 32 32 0
## order_purchase_timestamp 0 99441 99441 19 19 0
## order_status 0 99441 99441 7 11 0
## n_unique
## 99441
## 90734
## 81019
## 95665
## 459
## 99441
## 98875
## 8To convert date-time character strings, We’ll use mutate_at to convert the character-type date-time values back to the needed format.
# Define character to date-time converstion (from lubridate) in a function.
input_function <- function(x, na.rm=FALSE) (ymd_hms(x))
# Define Columns which need to converted from character to date-time.
input_columns <- c("order_purchase_timestamp",
"order_approved_at",
"order_delivered_carrier_date",
"order_delivered_customer_date",
"order_estimated_delivery_date")
# Use above defined inputs to mutate the select columns
df_orders_dt <- df_orders %>%
mutate_at(.vars = input_columns,
.funs = input_function)
#Inspect Conversion
df_orders_dt %>% glimpse()## Observations: 99,441
## Variables: 8
## $ order_id <chr> "e481f51cbdc54678b7cc49136f2d6af...
## $ customer_id <chr> "9ef432eb6251297304e76186b10a928...
## $ order_status <chr> "delivered", "delivered", "deliv...
## $ order_purchase_timestamp <dttm> 2017-10-02 10:56:33, 2018-07-24...
## $ order_approved_at <dttm> 2017-10-02 11:07:15, 2018-07-26...
## $ order_delivered_carrier_date <dttm> 2017-10-04 19:55:00, 2018-07-26...
## $ order_delivered_customer_date <dttm> 2017-10-10 21:25:13, 2018-08-07...
## $ order_estimated_delivery_date <dttm> 2017-10-18, 2018-08-13, 2018-09...I noticed there is a order_status variable. I’m curious what the different status conditions are, and their distribution. We’ll use Hmisc::describe to look deeper at that variable.
df_orders %>% select(order_status) %>% describe()## .
##
## 1 Variables 99441 Observations
## ---------------------------------------------------------------------------
## order_status
## n missing distinct
## 99441 0 8
##
## Value approved canceled created delivered invoiced
## Frequency 2 625 5 96478 314
## Proportion 0.000 0.006 0.000 0.970 0.003
##
## Value processing shipped unavailable
## Frequency 301 1107 609
## Proportion 0.003 0.011 0.006
## ---------------------------------------------------------------------------So 97% of all statuses are that the order was delivered. I suppose this is a very useful value from order to delivery, as the status might change. But looking at historical data, nearly all packages are delivered. Not much information provided by this variable.
Location Code Conversion
Some location code information has lost information. Some city zip codes and region codes have leading zero values. When they are imported into this notebook, they are imported as integers, and these leading zeros get dropped. But these codes function as strings. Therefore, we need to convert these codes back to strings and ensure any dropped leading zeros are reinserted.
The codes that need to be adjusted are: * Customer Zip * Seller Zip * Geo Location Zip * Municipal code (From Brazilian Population Data)
First we develop a function that takes a dataframe and column name.
Function
fun_pad_zero <- function(df, col_name){
col_name = enquo(col_name)
# Determine Max Code Length
max_len <-df %>%
mutate(!!quo_name(col_name) := as.character(!!col_name),
code_len = str_length(!!col_name)) %>%
summarise(max = max(code_len)) %>%
as.integer()
# Pad Zeros
df <- df %>%
mutate(!!quo_name(col_name) := str_pad(string = !!col_name,
width = max_len,
side = "left",
pad = 0))
return(df)
}Convert
Evaluate zero padding worked.
df_geo_loc %>% glimpse()## Observations: 1,000,163
## Variables: 5
## $ geolocation_zip_code_prefix <chr> "01037", "01046", "01046", "01041"...
## $ geolocation_lat <dbl> -23.54562, -23.54608, -23.54613, -...
## $ geolocation_lng <dbl> -46.63929, -46.64482, -46.64295, -...
## $ geolocation_city <chr> "sao paulo", "sao paulo", "sao pau...
## $ geolocation_state <chr> "SP", "SP", "SP", "SP", "SP", "SP"...df_cust %>% glimpse()## Observations: 99,441
## Variables: 5
## $ customer_id <chr> "06b8999e2fba1a1fbc88172c00ba8bc7", "...
## $ customer_unique_id <chr> "861eff4711a542e4b93843c6dd7febb0", "...
## $ customer_zip_code_prefix <chr> "14409", "09790", "01151", "08775", "...
## $ customer_city <chr> "franca", "sao bernardo do campo", "s...
## $ customer_state <chr> "SP", "SP", "SP", "SP", "SP", "SC", "...df_sellers %>% glimpse()## Observations: 3,095
## Variables: 4
## $ seller_id <chr> "3442f8959a84dea7ee197c632cb2df15", "d1...
## $ seller_zip_code_prefix <chr> "13023", "13844", "20031", "04195", "12...
## $ seller_city <chr> "campinas", "mogi guacu", "rio de janei...
## $ seller_state <chr> "SP", "SP", "RJ", "SP", "SP", "RJ", "PE...df_brazil_pop %>% glimpse()## Observations: 5,570
## Variables: 5
## $ uf <chr> "RO", "RO", "RO", "RO", "RO", "RO", "RO", "...
## $ cod_uf <int> 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,...
## $ cod_munic <chr> "00015", "00023", "00031", "00049", "00056"...
## $ nome_do_municipio <chr> "Alta Floresta D'Oeste", "Ariquemes", "Cabi...
## $ populacao_estimada <int> 22945, 107863, 5312, 85359, 16323, 15882, 7...Convert Population State Code to Character
The cod_uf variable in the Brazil populatin dataframe functions like a character variable. To correclty use it, we need to convert it to character.
df_brazil_pop <- df_brazil_pop %>%
mutate(cod_uf = as.character(cod_uf))
df_brazil_pop %>% glimpse()## Observations: 5,570
## Variables: 5
## $ uf <chr> "RO", "RO", "RO", "RO", "RO", "RO", "RO", "...
## $ cod_uf <chr> "11", "11", "11", "11", "11", "11", "11", "...
## $ cod_munic <chr> "00015", "00023", "00031", "00049", "00056"...
## $ nome_do_municipio <chr> "Alta Floresta D'Oeste", "Ariquemes", "Cabi...
## $ populacao_estimada <int> 22945, 107863, 5312, 85359, 16323, 15882, 7...Customer Zip
df_cust<- fun_pad_zero(df = df_cust,
col_name = customer_zip_code_prefix)
df_cust %>% glimpse()## Observations: 99,441
## Variables: 5
## $ customer_id <chr> "06b8999e2fba1a1fbc88172c00ba8bc7", "...
## $ customer_unique_id <chr> "861eff4711a542e4b93843c6dd7febb0", "...
## $ customer_zip_code_prefix <chr> "14409", "09790", "01151", "08775", "...
## $ customer_city <chr> "franca", "sao bernardo do campo", "s...
## $ customer_state <chr> "SP", "SP", "SP", "SP", "SP", "SC", "...Sales Breakdown By Categories
The following section will break down the total sales by certain factors. This information is meant to provide some basic descriptive information.
Product Category
There are 71 number of unique categories.
df_products_translate %>%
select(product_category_name_english) %>%
describe()## .
##
## 1 Variables 32951 Observations
## ---------------------------------------------------------------------------
## product_category_name_english
## n missing distinct
## 32328 623 71
##
## lowest : agro_industry_and_commerce air_conditioning art arts_and_craftmanship audio
## highest: stationery tablets_printing_image telephony toys watches_gifts
## ---------------------------------------------------------------------------This is an unweildy number of unique categories. Lets see what the frequency of each category being used is by sale. Maybe we can drop some. Otherwise, maybe we can group them.
category_by_sales <- df_order_items %>%
left_join(df_products_translate, by = "product_id") %>%
select(product_id, price, product_category_name_english) %>%
group_by(product_category_name_english) %>%
summarise(total_sales = sum(price)) %>%
ungroup() %>%
arrange(desc(total_sales)) %>%
mutate(percent_total = round((total_sales/sum(total_sales) * 100),2))
category_by_sales## # A tibble: 72 x 3
## product_category_name_english total_sales percent_total
## <chr> <dbl> <dbl>
## 1 health_beauty 1258681. 9.26
## 2 watches_gifts 1205006. 8.87
## 3 bed_bath_table 1036989. 7.63
## 4 sports_leisure 988049. 7.27
## 5 computers_accessories 911954. 6.71
## 6 furniture_decor 729762. 5.37
## 7 cool_stuff 635291. 4.67
## 8 housewares 632249. 4.65
## 9 auto 592720. 4.36
## 10 garden_tools 485256. 3.57
## # ... with 62 more rowsNote, the top 20 categories account for 84.02% of sales. While this is a large percentage, I do not think it is dominant enough to just drop other categories. Instead, a select number of superceding categories should be developed.
category_by_sales %>% head(20) %>%
select(-total_sales) %>%
summarise(n = sum(percent_total))## # A tibble: 1 x 1
## n
## <dbl>
## 1 84.0Product Id
We want to see what the highest selling items were, and what category they fall into.
df_products_translate## product_id product_category_name_english
## 1 7ea517e5 perfumery
## 2 641aaa2f art
## 3 1975717f sports_leisure
## 4 239eb23d baby
## 5 195d0b71 housewares
## 6 5283ed13 musical_instruments
## 7 7d81becb cool_stuff
## 8 8e9ab61e furniture_decor
## 9 77a21a02 home_appliances
## 10 63025574 toys
## 11 9c98dcb9 bed_bath_table
## 12 0c345514 baby
## 13 6e48fc22 musical_instruments
## 14 6660c7c2 furniture_decor
## 15 3d8c7415 construction_tools_safety
## 16 8a7b13b3 sports_leisure
## 17 284a004c perfumery
## 18 ba108a24 computers_accessories
## 19 578413b6 furniture_decor
## 20 e5ec51f0 bed_bath_table
## 21 c3302921 furniture_decor
## 22 94ba15d9 bed_bath_table
## 23 315c3f6e health_beauty
## 24 324d1bc4 baby
## 25 15e5e9ef furniture_decor
## 26 f710975e housewares
## 27 5abe2d06 luggage_accessories
## 28 87c28cd5 computers_accessories
## 29 546f1a32 computers_accessories
## 30 f296f80d sports_leisure
## 31 993bb74f bed_bath_table
## 32 32c12562 garden_tools
## 33 47a8648b furniture_decor
## 34 499ed4e1 office_furniture
## 35 3b747475 sports_leisure
## 36 74265584 baby
## 37 522572b9 auto
## 38 4b6fb479 electronics
## 39 74a110b9 housewares
## 40 bf417054 furniture_decor
## 41 d95fe1ea furniture_decor
## 42 57694866 toys
## 43 e29b87f9 bed_bath_table
## 44 f93e8735 baby
## 45 dcf03303 cool_stuff
## 46 01d6cbf3 toys
## 47 eb05bdc9 cool_stuff
## 48 19156f8f furniture_decor
## 49 96aece48 furniture_decor
## 50 8ecb56e8 health_beauty
## 51 682809fd sports_leisure
## 52 8f220ee7 cool_stuff
## 53 9636e244 auto
## 54 30cd9ef4 baby
## 55 3c3759e6 luggage_accessories
## 56 bc605dc4 fashion_shoes
## 57 8bac5cd0 telephony
## 58 d1d0287e cool_stuff
## 59 6979e296 stationery
## 60 47dd756d toys
## 61 0477b2b3 sports_leisure
## 62 27dd586c stationery
## 63 91445d39 health_beauty
## 64 0355a5d8 fashion_bags_accessories
## 65 53c76f95 stationery
## 66 dc54767b stationery
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## [ reached 'max' / getOption("max.print") -- omitted 21840 rows ]df_order_items## order_id order_item_id product_id
## 1 00010242fe8c5a6d1ba2dd792cb16214 1 83c13e61
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## 14227 e5a3438891c0bfdb9394643f95273d8e 2017-06-05 05:05:12 73.90
## 14228 48efc9d94a9834137efd9ea76b065a38 2017-01-09 13:59:30 9.90
## 14229 282f23a9769b2690c5dda22e316f9941 2017-12-21 02:51:50 40.80
## 14230 b33e7c55446eabf8fe1a42d037ac7d6d 2017-12-07 11:18:28 259.99
## 14231 cc63f0dd2acba93ffed4fe9f8e0321fa 2018-07-23 15:35:20 72.00
## 14232 cc63f0dd2acba93ffed4fe9f8e0321fa 2018-07-23 15:35:20 72.00
## 14233 3bb548e3cb7f70f28e3f11ee9dce0e59 2018-08-21 14:30:29 75.00
## 14234 1554a68530182680ad5c8b042c3ab563 2017-12-20 22:31:46 69.90
## 14235 9e7c5f4d7770eab65738cca38f9efccf 2018-08-13 01:44:31 84.90
## 14236 cc419e0650a3c5ba77189a1882b7556a 2017-12-11 15:13:46 56.99
## 14237 6560211a19b47992c3666cc44a7e94c0 2017-09-12 04:30:52 55.00
## 14238 88460e8ebdecbfecb5f9601833981930 2018-05-10 10:11:45 138.90
## 14239 88460e8ebdecbfecb5f9601833981930 2018-05-10 10:11:45 138.90
## 14240 7a67c85e85bb2ce8582c35f2203ad736 2018-06-04 19:15:12 89.99
## 14241 1b4c3a6f53068f0b6944d2d005c9fc89 2018-05-10 10:30:34 159.49
## 14242 4b9750c8ad28220fe6702d4ecb7c898f 2018-03-06 20:31:48 48.90
## 14243 ea8482cd71df3c1969d7b9473ff13abc 2018-05-24 05:16:33 21.99
## 14244 c4d51195486dc781531876a7d00453d8 2018-04-08 21:15:15 1224.99
## 14245 bc503e244f187939d10c92a5c8e378ce 2017-12-07 04:39:05 69.12
## 14246 bc503e244f187939d10c92a5c8e378ce 2017-12-07 04:39:05 69.12
## 14247 bc503e244f187939d10c92a5c8e378ce 2017-12-07 04:39:05 69.12
## 14248 7178f9f4dd81dcef02f62acdf8151e01 2017-10-10 02:14:42 79.00
## 14249 8b321bb669392f5163d04c59e235e066 2018-03-27 23:30:27 13.65
## 14250 f7ba60f8c3f99e7ee4042fdef03b70c4 2017-11-13 12:30:33 325.00
## 14251 70a12e78e608ac31179aea7f8422044b 2017-05-29 16:03:08 19.95
## 14252 b2ac621f0d0322434d04a12b078b9369 2017-10-26 14:21:20 205.00
## 14253 128639473a139ac0f3e5f5ade55873a5 2018-02-06 14:55:40 19.50
## 14254 da8622b14eb17ae2831f4ac5b9dab84a 2018-07-10 20:50:19 14.99
## 14255 dfa0c4c6229ab200a4a1336b4d7128ff 2018-06-18 04:31:24 39.00
## 14256 12b9676b00f60f3b700e83af21824c0e 2018-07-03 15:00:09 199.00
## 14257 bccf933e006e9b94a6184af782963e77 2018-02-28 00:30:06 149.99
## 14258 bccf933e006e9b94a6184af782963e77 2018-02-28 00:30:06 149.99
## 14259 669ae81880e08f269a64487cfb287169 2017-07-07 05:04:00 65.00
## 14260 1025f0e2d44d7041d6cf58b6550e0bfa 2018-03-20 02:50:51 20.70
## 14261 4a3ca9315b744ce9f8e9374361493884 2017-07-20 17:43:37 89.90
## 14262 16090f2ca825584b5a147ab24aa30c86 2018-02-05 10:55:54 27.90
## 14263 cd6efc47efaabf134f8bdb654e10b4f1 2018-04-25 17:35:51 127.00
## 14264 5670f4db5b62c43d542e1b2d56b0cf7c 2018-08-01 16:35:17 268.38
## 14265 3c7c4a49ec3c6550809089c6a2ca9370 2017-11-17 02:46:48 13.99
## 14266 de23c3b98a88888289c6f5cc1209054a 2018-04-11 02:10:42 89.00
## 14267 4c2b230173bb36f9b240f2b8ac11786e 2018-04-30 12:32:26 59.90
## 14268 7ddcbb64b5bc1ef36ca8c151f6ec77df 2018-01-08 21:50:27 166.99
## 14269 165fc07beebdcb6190fba8a06db2a449 2018-02-05 04:31:06 160.00
## 14270 850f4f8af5ea87287ac68de36e29107f 2017-05-05 09:50:13 59.99
## 14271 e21a1e8be4715b0fcfd5385643bc49c4 2017-06-01 14:30:23 19.90
## 14272 53243585a1d6dc2643021fd1853d8905 2017-12-14 18:19:34 189.90
## 14273 4d0041cdbe51b4617d95c70b19decc74 2018-06-22 13:31:51 1369.90
## 14274 92eb0f42c21942b6552362b9b114707d 2018-05-23 10:31:42 12.77
## 14275 1e8b33f18b4f7598d87f5cbee2282cc2 2018-03-21 16:11:13 76.90
## 14276 1e8b33f18b4f7598d87f5cbee2282cc2 2018-03-21 16:11:13 76.90
## 14277 b4f8921fcc4ff77b66bea6dda43dcd51 2018-08-14 18:50:19 599.99
## 14278 dc8798cbf453b7e0f98745e396cc5616 2017-09-25 03:30:47 24.90
## 14279 bd0389da23d89b726abf911cccc54596 2017-11-21 02:55:41 30.90
## 14280 ffa6adafb71b807dc13159e26431354c 2018-08-09 04:24:38 99.90
## 14281 4a3ca9315b744ce9f8e9374361493884 2018-03-04 21:48:16 422.00
## 14282 eec3469d83e142f9a104886655338c37 2017-04-03 09:02:48 10.90
## 14283 404e1ba01358af4cd63f679b2c4d1fa1 2017-08-25 01:33:25 29.90
## 14284 7e93a43ef30c4f03f38b393420bc753a 2017-06-13 16:30:24 288.28
## 14285 391fc6631aebcf3004804e51b40bcf1e 2018-03-27 20:55:49 39.99
## freight_value
## 1 13.29
## 2 19.93
## 3 17.87
## 4 12.79
## 5 18.14
## 6 12.69
## 7 11.85
## 8 70.75
## 9 11.65
## 10 11.40
## 11 8.88
## 12 12.98
## 13 23.32
## 14 13.37
## 15 13.37
## 16 27.65
## 17 11.34
## 18 8.77
## 19 13.71
## 20 16.11
## 21 8.88
## 22 11.85
## 23 44.40
## 24 15.10
## 25 16.60
## 26 16.98
## 27 26.33
## 28 25.51
## 29 7.94
## 30 17.32
## 31 24.65
## 32 23.35
## 33 15.10
## 34 15.10
## 35 15.10
## 36 14.10
## 37 15.10
## 38 16.11
## 39 20.80
## 40 44.29
## 41 13.72
## 42 19.95
## 43 17.63
## 44 17.63
## 45 17.63
## 46 8.54
## 47 13.01
## 48 13.71
## 49 7.78
## 50 7.78
## 51 16.79
## 52 18.00
## 53 18.21
## 54 15.20
## 55 21.05
## 56 15.10
## 57 13.21
## 58 16.49
## 59 18.23
## 60 43.11
## 61 14.10
## 62 29.39
## 63 21.75
## 64 16.43
## 65 15.80
## 66 13.46
## 67 23.22
## 68 19.18
## 69 15.31
## 70 15.59
## 71 21.62
## 72 18.54
## 73 21.11
## 74 65.56
## 75 19.67
## 76 8.90
## 77 8.90
## 78 14.84
## 79 34.23
## 80 23.31
## 81 32.57
## 82 7.16
## 83 17.67
## 84 67.24
## 85 15.38
## 86 34.07
## 87 27.19
## 88 14.45
## 89 14.45
## 90 16.89
## 91 8.21
## 92 9.94
## 93 9.94
## 94 28.58
## 95 18.44
## 96 12.11
## 97 18.54
## 98 14.10
## 99 66.04
## 100 23.64
## 101 11.85
## 102 18.58
## 103 18.58
## 104 33.33
## 105 15.28
## 106 18.08
## 107 12.90
## 108 8.89
## 109 35.74
## 110 27.75
## 111 50.85
## 112 17.03
## 113 17.03
## 114 9.80
## 115 0.00
## 116 34.39
## 117 33.91
## 118 7.78
## 119 15.37
## 120 16.14
## 121 13.84
## 122 15.51
## 123 15.73
## 124 7.78
## 125 85.97
## 126 10.96
## 127 17.60
## 128 8.03
## 129 11.85
## 130 61.18
## 131 27.39
## 132 11.98
## 133 15.61
## 134 19.75
## 135 13.93
## 136 17.89
## 137 17.89
## 138 8.40
## 139 8.40
## 140 8.40
## 141 8.40
## 142 40.37
## 143 16.79
## 144 15.01
## 145 15.01
## 146 12.27
## 147 25.90
## 148 13.75
## 149 14.28
## 150 14.52
## 151 14.84
## 152 18.12
## 153 13.58
## 154 13.58
## 155 16.60
## 156 16.25
## 157 54.02
## 158 20.31
## 159 16.22
## 160 31.17
## 161 8.27
## 162 11.85
## 163 87.28
## 164 13.71
## 165 20.85
## 166 16.17
## 167 15.79
## 168 15.79
## 169 16.44
## 170 13.89
## 171 20.80
## 172 14.14
## 173 11.99
## 174 7.78
## 175 7.39
## 176 14.10
## 177 43.06
## 178 30.35
## 179 12.79
## 180 14.06
## 181 12.46
## 182 14.90
## 183 8.16
## 184 15.71
## 185 10.96
## 186 14.83
## 187 82.86
## 188 7.39
## 189 13.95
## 190 18.69
## 191 16.00
## 192 15.10
## 193 16.11
## 194 14.10
## 195 10.70
## 196 17.95
## 197 15.24
## 198 23.13
## 199 19.03
## 200 11.73
## 201 10.63
## 202 7.65
## 203 16.69
## 204 18.23
## 205 18.23
## 206 13.00
## 207 9.44
## 208 27.39
## 209 36.85
## 210 15.23
## 211 15.23
## 212 34.15
## 213 8.74
## 214 21.44
## 215 37.09
## 216 15.56
## 217 7.53
## 218 11.62
## 219 25.66
## 220 18.57
## 221 14.35
## 222 7.87
## 223 7.39
## 224 39.41
## 225 14.80
## 226 42.76
## 227 13.47
## 228 12.44
## 229 7.86
## 230 8.59
## 231 10.68
## 232 31.16
## 233 47.40
## 234 21.49
## 235 18.23
## 236 18.23
## 237 12.92
## 238 23.34
## 239 15.10
## 240 15.10
## 241 34.15
## 242 15.34
## 243 45.12
## 244 8.82
## 245 8.86
## 246 41.53
## 247 7.75
## 248 7.78
## 249 7.39
## 250 11.85
## 251 22.99
## 252 21.43
## 253 22.56
## 254 22.56
## 255 22.56
## 256 29.08
## 257 18.50
## 258 14.10
## 259 0.00
## 260 12.27
## 261 22.30
## 262 14.51
## 263 12.48
## 264 7.53
## 265 7.46
## 266 65.34
## 267 23.57
## 268 19.32
## 269 30.66
## 270 19.25
## 271 9.26
## 272 25.73
## 273 11.85
## 274 17.87
## 275 23.33
## 276 23.33
## 277 13.68
## 278 26.61
## 279 26.26
## 280 7.68
## 281 15.23
## 282 16.11
## 283 16.60
## 284 14.59
## 285 11.85
## 286 21.19
## 287 11.15
## 288 39.66
## 289 39.66
## 290 23.07
## 291 15.10
## 292 16.60
## 293 14.10
## 294 19.01
## 295 7.88
## 296 15.69
## 297 15.80
## 298 52.69
## 299 15.80
## 300 15.80
## 301 13.81
## 302 7.80
## 303 12.48
## 304 18.93
## 305 20.18
## 306 7.39
## 307 20.39
## 308 16.61
## 309 16.61
## 310 12.48
## 311 16.79
## 312 15.10
## 313 13.07
## 314 67.69
## 315 7.39
## 316 18.35
## 317 14.65
## 318 14.10
## 319 13.91
## 320 17.67
## 321 15.10
## 322 15.20
## 323 25.13
## 324 18.00
## 325 25.12
## 326 11.74
## 327 15.10
## 328 15.10
## 329 15.10
## 330 15.10
## 331 18.13
## 332 26.84
## 333 40.37
## 334 14.10
## 335 9.17
## 336 14.68
## 337 14.59
## 338 15.11
## 339 21.17
## 340 18.69
## 341 19.00
## 342 22.34
## 343 20.18
## 344 20.18
## 345 23.52
## 346 11.85
## 347 14.52
## 348 34.22
## 349 34.22
## 350 15.33
## 351 9.26
## 352 13.77
## 353 97.56
## 354 35.98
## 355 20.80
## 356 20.80
## 357 29.30
## 358 33.23
## 359 12.84
## 360 35.07
## 361 14.82
## 362 15.30
## 363 24.56
## 364 17.69
## 365 23.17
## 366 19.33
## 367 14.10
## 368 11.85
## 369 11.85
## 370 15.37
## 371 16.00
## 372 19.03
## 373 21.85
## 374 21.88
## 375 13.33
## 376 16.11
## 377 17.07
## 378 14.96
## 379 16.11
## 380 13.26
## 381 13.37
## 382 21.18
## 383 22.97
## 384 14.80
## 385 19.98
## 386 16.35
## 387 9.34
## 388 9.34
## 389 9.34
## 390 9.34
## 391 23.35
## 392 9.27
## 393 17.22
## 394 21.43
## 395 15.11
## 396 23.65
## 397 60.81
## 398 32.07
## 399 32.79
## 400 19.44
## 401 19.44
## 402 19.44
## 403 14.52
## 404 35.67
## 405 18.38
## 406 18.38
## 407 18.38
## 408 14.77
## 409 9.34
## 410 19.72
## 411 9.71
## 412 18.26
## 413 16.79
## 414 16.79
## 415 10.96
## 416 16.11
## 417 18.40
## 418 20.84
## 419 17.60
## 420 8.02
## 421 26.10
## 422 26.10
## 423 15.25
## 424 10.29
## 425 18.14
## 426 8.72
## 427 11.85
## 428 10.90
## 429 15.85
## 430 35.37
## 431 26.71
## 432 28.53
## 433 20.12
## 434 28.96
## 435 9.06
## 436 15.15
## 437 8.72
## 438 34.15
## 439 34.15
## 440 34.15
## 441 34.15
## 442 14.12
## 443 16.79
## 444 20.65
## 445 15.23
## 446 15.48
## 447 17.67
## 448 17.67
## 449 14.51
## 450 18.65
## 451 18.43
## 452 14.10
## 453 13.24
## 454 79.94
## 455 13.82
## 456 43.49
## 457 98.13
## 458 19.34
## 459 19.34
## 460 78.63
## 461 16.81
## 462 63.11
## 463 15.44
## 464 15.44
## 465 7.87
## 466 24.70
## 467 24.70
## 468 10.13
## 469 15.10
## 470 11.98
## 471 22.63
## 472 16.47
## 473 14.10
## 474 14.70
## 475 16.06
## 476 21.20
## 477 17.28
## 478 16.72
## 479 15.16
## 480 53.83
## 481 16.50
## 482 19.32
## 483 19.32
## 484 0.00
## 485 8.29
## 486 16.53
## 487 14.10
## 488 19.25
## 489 16.11
## 490 16.79
## 491 27.41
## 492 15.83
## 493 15.29
## 494 17.18
## 495 5.31
## 496 9.57
## 497 18.56
## 498 33.93
## 499 33.93
## 500 12.48
## 501 17.06
## 502 17.26
## 503 16.94
## 504 18.83
## 505 18.00
## 506 12.65
## 507 12.71
## 508 12.71
## 509 0.00
## 510 0.00
## 511 15.10
## 512 28.99
## 513 8.88
## 514 15.10
## 515 11.15
## 516 15.32
## 517 12.90
## 518 14.08
## 519 81.62
## 520 14.52
## 521 14.70
## 522 38.47
## 523 13.41
## 524 17.88
## 525 17.88
## 526 21.35
## 527 8.36
## 528 8.88
## 529 25.73
## 530 18.21
## 531 17.98
## 532 7.78
## 533 16.79
## 534 16.79
## 535 16.32
## 536 10.96
## 537 23.35
## 538 23.35
## 539 19.21
## 540 16.95
## 541 8.72
## 542 15.10
## 543 16.07
## 544 13.11
## 545 14.73
## 546 16.74
## 547 18.23
## 548 46.67
## 549 46.67
## 550 11.90
## 551 3.81
## 552 25.39
## 553 19.72
## 554 14.10
## 555 23.31
## 556 23.31
## 557 23.31
## 558 23.31
## 559 11.85
## 560 11.85
## 561 11.85
## 562 11.85
## 563 11.85
## 564 23.69
## 565 8.88
## 566 18.24
## 567 23.75
## 568 15.72
## 569 12.69
## 570 12.69
## 571 14.10
## 572 14.31
## 573 18.34
## 574 7.78
## 575 15.30
## 576 20.51
## 577 12.69
## 578 20.09
## 579 16.71
## 580 14.14
## 581 37.54
## 582 38.45
## 583 7.78
## 584 53.11
## 585 18.23
## 586 11.97
## 587 15.30
## 588 8.37
## 589 9.26
## 590 26.54
## 591 94.98
## 592 94.98
## 593 9.09
## 594 25.54
## 595 34.15
## 596 24.90
## 597 15.10
## 598 7.78
## 599 16.36
## 600 9.76
## 601 7.78
## 602 15.14
## 603 7.78
## 604 7.87
## 605 23.55
## 606 8.30
## 607 22.68
## 608 12.09
## 609 22.58
## 610 22.58
## 611 17.65
## 612 36.96
## 613 11.85
## 614 16.67
## 615 34.15
## 616 17.19
## 617 7.78
## 618 7.78
## 619 18.52
## 620 20.02
## 621 18.23
## 622 7.78
## 623 21.26
## 624 17.85
## 625 19.42
## 626 24.11
## 627 25.81
## 628 15.10
## 629 16.79
## 630 10.70
## 631 26.75
## 632 7.78
## 633 16.11
## 634 16.11
## 635 15.17
## 636 9.34
## 637 7.39
## 638 7.39
## 639 7.39
## 640 7.39
## 641 15.33
## 642 15.34
## 643 15.28
## 644 17.40
## 645 34.15
## 646 16.39
## 647 25.96
## 648 17.60
## 649 9.09
## 650 9.09
## 651 11.74
## 652 18.41
## 653 23.25
## 654 15.10
## 655 15.15
## 656 16.16
## 657 8.27
## 658 8.27
## 659 15.11
## 660 18.31
## 661 9.94
## 662 43.40
## 663 9.09
## 664 29.07
## 665 18.28
## 666 17.10
## 667 25.65
## 668 38.46
## 669 27.96
## 670 20.17
## 671 85.59
## 672 85.59
## 673 57.94
## 674 15.39
## 675 12.97
## 676 14.49
## 677 16.94
## 678 35.67
## 679 16.96
## 680 12.76
## 681 21.71
## 682 15.10
## 683 58.91
## 684 28.50
## 685 29.34
## 686 15.13
## 687 8.72
## 688 7.39
## 689 8.00
## 690 18.53
## 691 7.79
## 692 24.05
## 693 13.72
## 694 19.32
## 695 19.32
## 696 18.00
## 697 15.81
## 698 27.51
## 699 29.23
## 700 15.11
## 701 25.62
## 702 8.88
## 703 24.50
## 704 24.50
## 705 24.50
## 706 38.83
## 707 58.11
## 708 57.31
## 709 14.35
## 710 8.44
## 711 7.71
## 712 13.69
## 713 20.54
## 714 15.10
## 715 15.10
## 716 9.36
## 717 9.36
## 718 15.37
## 719 12.72
## 720 8.27
## 721 17.38
## 722 16.60
## 723 18.70
## 724 34.69
## 725 8.74
## 726 13.03
## 727 13.03
## 728 19.74
## 729 44.97
## 730 16.17
## 731 10.68
## 732 17.66
## 733 21.19
## 734 15.41
## 735 24.86
## 736 18.28
## 737 15.52
## 738 22.98
## 739 15.10
## 740 11.87
## 741 12.79
## 742 12.69
## 743 21.66
## 744 41.92
## 745 25.61
## 746 14.52
## 747 33.59
## 748 33.59
## 749 33.59
## 750 9.41
## 751 9.41
## 752 9.94
## 753 18.36
## 754 12.69
## 755 19.41
## 756 11.15
## 757 15.10
## 758 15.10
## 759 25.66
## 760 14.85
## 761 12.13
## 762 12.13
## 763 56.93
## 764 16.79
## 765 12.99
## 766 18.23
## 767 31.60
## 768 18.02
## 769 35.74
## 770 15.10
## 771 11.81
## 772 17.44
## 773 16.94
## 774 17.80
## 775 18.23
## 776 13.41
## 777 7.78
## 778 26.04
## 779 16.86
## 780 7.78
## 781 17.87
## 782 29.11
## 783 29.11
## 784 38.30
## 785 22.06
## 786 8.64
## 787 17.06
## 788 17.06
## 789 25.24
## 790 2.27
## 791 59.78
## 792 15.26
## 793 12.72
## 794 14.10
## 795 46.39
## 796 16.79
## 797 14.62
## 798 27.84
## 799 13.54
## 800 34.16
## 801 18.23
## 802 15.21
## 803 43.98
## 804 18.91
## 805 16.23
## 806 12.69
## 807 17.77
## 808 14.88
## 809 16.11
## 810 23.63
## 811 9.07
## 812 12.99
## 813 12.99
## 814 20.02
## 815 21.29
## 816 15.10
## 817 17.60
## 818 17.60
## 819 16.63
## 820 7.87
## 821 12.04
## 822 14.59
## 823 16.05
## 824 20.02
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## 827 11.85
## 828 12.83
## 829 14.10
## 830 12.84
## 831 15.10
## 832 12.48
## 833 12.48
## 834 25.78
## 835 11.85
## 836 15.10
## 837 16.46
## 838 17.21
## 839 18.63
## 840 16.02
## 841 8.64
## 842 10.96
## 843 18.52
## 844 19.32
## 845 18.65
## 846 68.13
## 847 45.30
## 848 14.59
## 849 15.79
## 850 8.29
## 851 14.43
## 852 20.59
## 853 7.82
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## 855 17.74
## 856 15.10
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## 859 23.38
## 860 23.91
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## 862 15.10
## 863 35.83
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## 865 13.18
## 866 102.63
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## 869 8.72
## 870 17.64
## 871 17.64
## 872 18.38
## 873 13.37
## 874 27.34
## 875 17.68
## 876 11.92
## 877 7.78
## 878 7.39
## 879 19.39
## 880 17.94
## 881 21.82
## 882 17.76
## 883 17.71
## 884 9.18
## 885 14.30
## 886 18.98
## 887 8.88
## 888 14.79
## 889 16.39
## 890 8.11
## 891 27.44
## 892 15.32
## 893 28.99
## 894 19.36
## 895 8.35
## 896 8.35
## 897 8.35
## 898 14.10
## 899 19.84
## 900 19.84
## 901 8.93
## 902 29.10
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## 904 14.63
## 905 17.95
## 906 22.99
## 907 22.99
## 908 15.11
## 909 10.74
## 910 10.74
## 911 9.45
## 912 14.53
## 913 12.09
## 914 55.36
## 915 12.69
## 916 12.69
## 917 12.69
## 918 11.85
## 919 15.10
## 920 16.79
## 921 17.63
## 922 27.26
## 923 8.88
## 924 14.10
## 925 11.62
## 926 28.63
## 927 13.90
## 928 22.23
## 929 12.19
## 930 13.82
## 931 52.83
## 932 13.66
## 933 7.39
## 934 13.71
## 935 12.84
## 936 16.39
## 937 13.71
## 938 18.27
## 939 9.53
## 940 14.33
## 941 98.77
## 942 21.09
## 943 21.15
## 944 27.13
## 945 11.85
## 946 17.53
## 947 16.66
## 948 19.59
## 949 25.69
## 950 23.25
## 951 16.56
## 952 19.76
## 953 66.63
## 954 16.17
## 955 13.84
## 956 22.10
## 957 32.49
## 958 3.25
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## 960 22.85
## 961 7.39
## 962 7.39
## 963 14.05
## 964 7.94
## 965 16.25
## 966 74.99
## 967 34.98
## 968 17.03
## 969 19.07
## 970 15.59
## 971 11.62
## 972 17.06
## 973 17.06
## 974 30.84
## 975 33.82
## 976 13.13
## 977 12.33
## 978 18.76
## 979 20.01
## 980 18.30
## 981 15.11
## 982 45.09
## 983 23.98
## 984 25.37
## 985 7.46
## 986 34.04
## 987 21.33
## 988 14.10
## 989 14.10
## 990 16.18
## 991 16.16
## 992 16.16
## 993 8.21
## 994 7.78
## 995 52.85
## 996 52.85
## 997 18.35
## 998 12.31
## 999 17.07
## 1000 50.92
## 1001 12.52
## 1002 7.81
## 1003 19.59
## 1004 19.59
## 1005 9.44
## 1006 9.44
## 1007 11.85
## 1008 18.71
## 1009 15.13
## 1010 26.43
## 1011 27.60
## 1012 10.75
## 1013 14.13
## 1014 12.96
## 1015 19.53
## 1016 14.10
## 1017 36.24
## 1018 20.80
## 1019 20.80
## 1020 14.15
## 1021 12.14
## 1022 13.65
## 1023 9.94
## 1024 18.54
## 1025 15.10
## 1026 24.84
## 1027 7.78
## 1028 15.63
## 1029 15.11
## 1030 17.80
## 1031 29.46
## 1032 33.17
## 1033 15.12
## 1034 16.79
## 1035 16.95
## 1036 15.11
## 1037 27.89
## 1038 7.78
## 1039 22.34
## 1040 31.95
## 1041 13.57
## 1042 16.11
## 1043 16.96
## 1044 16.89
## 1045 17.76
## 1046 25.05
## 1047 15.65
## 1048 15.57
## 1049 19.59
## 1050 29.99
## 1051 69.90
## 1052 16.40
## 1053 8.72
## 1054 16.85
## 1055 16.79
## 1056 16.79
## 1057 15.52
## 1058 7.78
## 1059 23.84
## 1060 14.14
## 1061 14.37
## 1062 12.07
## 1063 17.63
## 1064 16.79
## 1065 40.48
## 1066 11.91
## 1067 15.10
## 1068 18.30
## 1069 14.47
## 1070 34.27
## 1071 16.18
## 1072 11.86
## 1073 11.86
## 1074 13.92
## 1075 54.12
## 1076 17.12
## 1077 18.41
## 1078 16.11
## 1079 24.09
## 1080 31.12
## 1081 9.33
## 1082 11.85
## 1083 75.01
## 1084 50.15
## 1085 134.17
## 1086 15.37
## 1087 23.57
## 1088 7.78
## 1089 16.18
## 1090 22.73
## 1091 17.84
## 1092 8.65
## 1093 15.25
## 1094 19.64
## 1095 17.63
## 1096 18.02
## 1097 37.12
## 1098 15.01
## 1099 16.17
## 1100 11.73
## 1101 17.02
## 1102 15.10
## 1103 15.38
## 1104 7.39
## 1105 7.87
## 1106 25.04
## 1107 14.10
## 1108 17.60
## 1109 16.98
## 1110 11.15
## 1111 18.59
## 1112 22.10
## 1113 11.90
## 1114 11.85
## 1115 16.50
## 1116 15.10
## 1117 17.14
## 1118 15.87
## 1119 15.87
## 1120 18.31
## 1121 63.63
## 1122 11.72
## 1123 294.76
## 1124 21.73
## 1125 17.74
## 1126 15.23
## 1127 16.25
## 1128 8.27
## 1129 8.27
## 1130 18.23
## 1131 18.23
## 1132 12.04
## 1133 26.95
## 1134 26.95
## 1135 26.95
## 1136 26.95
## 1137 26.95
## 1138 39.58
## 1139 25.04
## 1140 22.50
## 1141 26.27
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## 1146 15.11
## 1147 16.79
## 1148 8.29
## 1149 11.98
## 1150 11.50
## 1151 12.73
## 1152 6.53
## 1153 14.10
## 1154 16.21
## 1155 35.74
## 1156 19.56
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## 1159 14.10
## 1160 20.78
## 1161 2.91
## 1162 17.67
## 1163 21.57
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## 1166 49.85
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## 1169 13.08
## 1170 13.40
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## 1172 22.85
## 1173 13.08
## 1174 13.08
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## 1176 16.60
## 1177 8.72
## 1178 33.75
## 1179 23.18
## 1180 25.70
## 1181 50.00
## 1182 26.84
## 1183 1.80
## 1184 8.63
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## 1186 51.00
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## 1193 17.09
## 1194 17.09
## 1195 15.10
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## 1197 15.55
## 1198 13.57
## 1199 43.91
## 1200 23.29
## 1201 18.25
## 1202 18.25
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## 1204 20.77
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## 1208 15.23
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## 1210 9.99
## 1211 11.85
## 1212 57.59
## 1213 37.04
## 1214 11.74
## 1215 9.67
## 1216 21.49
## 1217 13.00
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## 1221 9.34
## 1222 9.34
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## 1224 15.79
## 1225 14.08
## 1226 10.01
## 1227 7.39
## 1228 12.26
## 1229 18.32
## 1230 13.03
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## 1232 21.15
## 1233 15.52
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## 1236 27.21
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## 1239 12.79
## 1240 12.79
## 1241 12.79
## 1242 12.79
## 1243 17.67
## 1244 17.67
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## 1272 40.19
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## 1288 15.44
## 1289 15.59
## 1290 9.93
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## 1292 16.16
## 1293 29.30
## 1294 12.79
## 1295 16.11
## 1296 15.16
## 1297 19.04
## 1298 13.58
## 1299 9.57
## 1300 12.90
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## 1310 15.63
## 1311 20.80
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## 1313 11.95
## 1314 19.05
## 1315 15.42
## 1316 10.13
## 1317 18.23
## 1318 18.23
## 1319 18.23
## 1320 21.56
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## 1324 15.10
## 1325 16.67
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## 1332 8.72
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## 1335 19.99
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## 1353 23.10
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## 1356 13.71
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## 1358 13.00
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## 1360 11.74
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## 1362 12.11
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## 1364 21.65
## 1365 15.10
## 1366 13.37
## 1367 33.85
## 1368 17.15
## 1369 12.79
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## 1372 26.88
## 1373 26.88
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## 1378 35.18
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## 1400 20.73
## 1401 14.21
## 1402 14.20
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## 1412 8.49
## 1413 8.49
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## 1419 22.06
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## 1421 12.62
## 1422 57.21
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## 1424 18.01
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## 1428 7.78
## 1429 7.78
## 1430 15.10
## 1431 15.10
## 1432 20.99
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## 1435 17.04
## 1436 17.03
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## 1441 15.79
## 1442 16.39
## 1443 15.19
## 1444 45.09
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## 1446 17.37
## 1447 18.59
## 1448 13.65
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## 1450 15.74
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## 1452 23.58
## 1453 18.23
## 1454 165.32
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## 1456 19.19
## 1457 70.54
## 1458 64.48
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## 1460 24.84
## 1461 8.29
## 1462 38.04
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## 1465 7.78
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## 1467 14.52
## 1468 14.52
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## 1471 64.49
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## 1473 18.10
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## 1493 21.46
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## 1495 26.86
## 1496 15.24
## 1497 14.10
## 1498 15.00
## 1499 27.78
## 1500 27.78
## 1501 27.78
## 1502 27.78
## 1503 27.78
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## 1511 16.45
## 1512 13.00
## 1513 17.06
## 1514 15.56
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## 1518 42.91
## 1519 21.89
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## 1524 12.04
## 1525 21.25
## 1526 21.04
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## 1528 12.92
## 1529 7.39
## 1530 19.50
## 1531 7.78
## 1532 16.96
## 1533 15.10
## 1534 15.49
## 1535 23.34
## 1536 18.44
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## 1538 33.08
## 1539 24.35
## 1540 9.47
## 1541 13.10
## 1542 8.51
## 1543 15.23
## 1544 11.85
## 1545 7.40
## 1546 22.57
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## 1548 15.59
## 1549 8.53
## 1550 17.41
## 1551 17.41
## 1552 22.80
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## 1554 25.30
## 1555 17.84
## 1556 17.80
## 1557 47.21
## 1558 18.71
## 1559 14.14
## 1560 13.40
## 1561 38.67
## 1562 50.99
## 1563 11.73
## 1564 27.67
## 1565 8.46
## 1566 11.90
## 1567 25.25
## 1568 27.30
## 1569 8.11
## 1570 23.05
## 1571 14.22
## 1572 14.58
## 1573 14.58
## 1574 12.69
## 1575 13.67
## 1576 18.23
## 1577 17.08
## 1578 25.75
## 1579 17.74
## 1580 73.14
## 1581 17.02
## 1582 17.02
## 1583 14.97
## 1584 25.81
## 1585 10.21
## 1586 16.42
## 1587 14.93
## 1588 19.60
## 1589 23.50
## 1590 18.91
## 1591 15.10
## 1592 8.06
## 1593 14.41
## 1594 30.44
## 1595 30.44
## 1596 30.44
## 1597 30.44
## 1598 30.44
## 1599 30.44
## 1600 37.30
## 1601 21.13
## 1602 18.12
## 1603 15.30
## 1604 18.51
## 1605 23.46
## 1606 22.85
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## 1608 46.47
## 1609 27.13
## 1610 23.14
## 1611 17.06
## 1612 17.06
## 1613 42.86
## 1614 17.92
## 1615 9.27
## 1616 18.30
## 1617 16.64
## 1618 15.10
## 1619 16.60
## 1620 24.97
## 1621 19.50
## 1622 14.10
## 1623 12.76
## 1624 34.29
## 1625 34.29
## 1626 19.59
## 1627 19.59
## 1628 7.78
## 1629 69.01
## 1630 9.09
## 1631 14.37
## 1632 21.15
## 1633 23.68
## 1634 11.15
## 1635 11.15
## 1636 17.14
## 1637 12.80
## 1638 16.21
## 1639 18.44
## 1640 14.95
## 1641 16.14
## 1642 16.95
## 1643 20.84
## 1644 73.41
## 1645 15.27
## 1646 16.11
## 1647 31.66
## 1648 28.01
## 1649 28.01
## 1650 28.01
## 1651 28.01
## 1652 28.01
## 1653 28.01
## 1654 28.01
## 1655 28.01
## 1656 19.03
## 1657 33.14
## 1658 59.16
## 1659 11.99
## 1660 16.40
## 1661 15.78
## 1662 19.04
## 1663 14.10
## 1664 23.12
## 1665 21.88
## 1666 9.34
## 1667 36.43
## 1668 14.60
## 1669 39.24
## 1670 11.15
## 1671 18.67
## 1672 8.29
## 1673 52.71
## 1674 43.22
## 1675 17.19
## 1676 17.19
## 1677 17.19
## 1678 12.93
## 1679 10.53
## 1680 12.38
## 1681 12.38
## 1682 16.25
## 1683 13.81
## 1684 13.81
## 1685 15.95
## 1686 15.10
## 1687 19.94
## 1688 9.34
## 1689 25.68
## 1690 15.79
## 1691 7.39
## 1692 7.78
## 1693 16.18
## 1694 15.16
## 1695 15.10
## 1696 13.37
## 1697 13.47
## 1698 17.63
## 1699 16.11
## 1700 15.94
## 1701 11.85
## 1702 23.98
## 1703 19.84
## 1704 17.31
## 1705 12.51
## 1706 9.00
## 1707 28.75
## 1708 24.01
## 1709 24.01
## 1710 15.57
## 1711 11.73
## 1712 17.84
## 1713 8.88
## 1714 58.78
## 1715 18.05
## 1716 12.98
## 1717 20.43
## 1718 16.28
## 1719 15.20
## 1720 27.32
## 1721 1.37
## 1722 12.82
## 1723 8.33
## 1724 14.10
## 1725 23.45
## 1726 13.71
## 1727 13.71
## 1728 18.20
## 1729 9.77
## 1730 9.77
## 1731 21.09
## 1732 17.90
## 1733 17.67
## 1734 12.07
## 1735 12.07
## 1736 45.01
## 1737 45.01
## 1738 15.10
## 1739 12.68
## 1740 8.27
## 1741 10.96
## 1742 17.45
## 1743 8.98
## 1744 17.35
## 1745 18.23
## 1746 19.82
## 1747 16.21
## 1748 10.96
## 1749 12.25
## 1750 7.96
## 1751 7.96
## 1752 169.12
## 1753 25.38
## 1754 11.74
## 1755 11.73
## 1756 14.52
## 1757 13.98
## 1758 36.51
## 1759 12.13
## 1760 15.29
## 1761 66.31
## 1762 18.23
## 1763 27.20
## 1764 12.92
## 1765 19.60
## 1766 13.83
## 1767 13.83
## 1768 21.01
## 1769 13.81
## 1770 17.92
## 1771 17.92
## 1772 25.82
## 1773 8.06
## 1774 15.32
## 1775 26.70
## 1776 8.65
## 1777 15.14
## 1778 16.11
## 1779 13.47
## 1780 20.61
## 1781 18.64
## 1782 11.85
## 1783 13.00
## 1784 37.69
## 1785 0.00
## 1786 18.23
## 1787 18.23
## 1788 19.26
## 1789 14.10
## 1790 7.68
## 1791 26.56
## 1792 13.98
## 1793 17.64
## 1794 19.14
## 1795 19.68
## 1796 19.68
## 1797 15.10
## 1798 8.75
## 1799 8.75
## 1800 14.93
## 1801 22.73
## 1802 9.34
## 1803 75.33
## 1804 19.87
## 1805 23.04
## 1806 7.92
## 1807 7.78
## 1808 16.60
## 1809 18.72
## 1810 19.04
## 1811 25.61
## 1812 17.25
## 1813 20.25
## 1814 15.10
## 1815 17.92
## 1816 17.92
## 1817 14.16
## 1818 54.72
## 1819 22.85
## 1820 9.70
## 1821 9.70
## 1822 11.85
## 1823 19.49
## 1824 29.70
## 1825 29.70
## 1826 25.68
## 1827 14.73
## 1828 26.98
## 1829 15.94
## 1830 14.72
## 1831 21.26
## 1832 21.26
## 1833 21.26
## 1834 15.10
## 1835 22.67
## 1836 12.94
## 1837 33.39
## 1838 11.85
## 1839 8.47
## 1840 18.35
## 1841 19.82
## 1842 12.80
## 1843 23.10
## 1844 8.88
## 1845 28.20
## 1846 22.87
## 1847 7.78
## 1848 15.43
## 1849 14.47
## 1850 14.47
## 1851 17.16
## 1852 17.16
## 1853 7.39
## 1854 16.11
## 1855 16.11
## 1856 16.11
## 1857 7.39
## 1858 7.39
## 1859 44.48
## 1860 14.10
## 1861 15.24
## 1862 34.20
## 1863 20.72
## 1864 9.77
## 1865 18.56
## 1866 15.23
## 1867 17.17
## 1868 19.24
## 1869 19.24
## 1870 15.22
## 1871 17.88
## 1872 17.60
## 1873 8.65
## 1874 16.79
## 1875 17.35
## 1876 18.23
## 1877 13.77
## 1878 13.86
## 1879 20.03
## 1880 8.84
## 1881 25.67
## 1882 15.11
## 1883 12.94
## 1884 21.30
## 1885 14.15
## 1886 25.23
## 1887 31.02
## 1888 19.86
## 1889 12.10
## 1890 17.60
## 1891 27.06
## 1892 11.85
## 1893 11.85
## 1894 15.11
## 1895 16.70
## 1896 19.49
## 1897 14.06
## 1898 20.06
## 1899 15.49
## 1900 7.53
## 1901 15.10
## 1902 15.10
## 1903 24.94
## 1904 25.63
## 1905 23.14
## 1906 18.23
## 1907 14.54
## 1908 20.40
## 1909 11.91
## 1910 9.47
## 1911 13.13
## 1912 21.68
## 1913 34.25
## 1914 7.78
## 1915 12.98
## 1916 52.99
## 1917 12.79
## 1918 29.07
## 1919 11.85
## 1920 18.40
## 1921 16.09
## 1922 15.23
## 1923 12.79
## 1924 17.97
## 1925 19.69
## 1926 19.44
## 1927 33.52
## 1928 15.36
## 1929 13.58
## 1930 33.52
## 1931 16.44
## 1932 16.86
## 1933 13.50
## 1934 11.85
## 1935 15.13
## 1936 18.31
## 1937 37.96
## 1938 8.99
## 1939 19.39
## 1940 18.23
## 1941 18.23
## 1942 8.93
## 1943 8.91
## 1944 15.31
## 1945 25.88
## 1946 23.03
## 1947 14.10
## 1948 14.10
## 1949 19.53
## 1950 8.29
## 1951 9.17
## 1952 34.61
## 1953 15.11
## 1954 15.11
## 1955 16.60
## 1956 14.32
## 1957 8.40
## 1958 17.94
## 1959 15.24
## 1960 18.43
## 1961 26.41
## 1962 13.69
## 1963 9.86
## 1964 17.43
## 1965 19.40
## 1966 15.23
## 1967 15.23
## 1968 9.06
## 1969 16.05
## 1970 13.37
## 1971 13.37
## 1972 53.05
## 1973 14.31
## 1974 24.71
## 1975 12.44
## 1976 61.51
## 1977 8.72
## 1978 16.50
## 1979 17.61
## 1980 63.11
## 1981 26.86
## 1982 15.94
## 1983 18.99
## 1984 13.71
## 1985 13.71
## 1986 13.71
## 1987 13.71
## 1988 13.71
## 1989 12.74
## 1990 58.78
## 1991 17.70
## 1992 47.17
## 1993 21.60
## 1994 13.78
## 1995 13.22
## 1996 7.87
## 1997 8.79
## 1998 15.14
## 1999 55.65
## 2000 23.50
## 2001 15.54
## 2002 12.61
## 2003 13.70
## 2004 14.61
## 2005 19.42
## 2006 22.59
## 2007 7.78
## 2008 14.21
## 2009 16.89
## 2010 17.73
## 2011 58.63
## 2012 14.58
## 2013 37.90
## 2014 15.35
## 2015 8.27
## 2016 19.36
## 2017 19.36
## 2018 19.35
## 2019 117.35
## 2020 5.52
## 2021 10.04
## 2022 7.95
## 2023 19.66
## 2024 17.36
## 2025 16.11
## 2026 17.06
## 2027 8.72
## 2028 11.17
## 2029 34.15
## 2030 16.32
## 2031 29.46
## 2032 22.16
## 2033 10.54
## 2034 10.54
## 2035 10.54
## 2036 10.54
## 2037 10.54
## 2038 10.54
## 2039 10.54
## 2040 10.54
## 2041 7.78
## 2042 15.58
## 2043 33.12
## 2044 13.08
## 2045 15.44
## 2046 7.81
## 2047 48.24
## 2048 15.65
## 2049 18.56
## 2050 12.79
## 2051 25.61
## 2052 23.36
## 2053 19.38
## 2054 17.06
## 2055 13.78
## 2056 13.78
## 2057 14.51
## 2058 60.16
## 2059 15.34
## 2060 21.60
## 2061 5.56
## 2062 7.78
## 2063 15.18
## 2064 14.10
## 2065 25.29
## 2066 18.59
## 2067 58.20
## 2068 16.11
## 2069 16.11
## 2070 16.11
## 2071 16.11
## 2072 23.28
## 2073 7.39
## 2074 13.46
## 2075 13.97
## 2076 19.62
## 2077 7.59
## 2078 13.50
## 2079 7.39
## 2080 18.69
## 2081 15.59
## 2082 15.90
## 2083 17.67
## 2084 56.09
## 2085 17.60
## 2086 12.79
## 2087 8.99
## 2088 15.36
## 2089 26.89
## 2090 16.46
## 2091 16.46
## 2092 1.98
## 2093 15.39
## 2094 12.79
## 2095 15.22
## 2096 19.32
## 2097 14.52
## 2098 15.24
## 2099 14.72
## 2100 16.74
## 2101 16.18
## 2102 11.73
## 2103 8.29
## 2104 15.43
## 2105 38.74
## 2106 19.52
## 2107 19.75
## 2108 7.78
## 2109 15.45
## 2110 29.71
## 2111 15.14
## 2112 35.14
## 2113 21.85
## 2114 12.89
## 2115 15.10
## 2116 11.16
## 2117 23.43
## 2118 17.25
## 2119 11.85
## 2120 2.46
## 2121 24.63
## 2122 12.69
## 2123 20.86
## 2124 12.79
## 2125 23.64
## 2126 10.66
## 2127 12.08
## 2128 8.11
## 2129 8.11
## 2130 51.19
## 2131 26.38
## 2132 18.43
## 2133 6.09
## 2134 12.17
## 2135 7.78
## 2136 17.54
## 2137 7.78
## 2138 14.39
## 2139 13.79
## 2140 15.10
## 2141 11.85
## 2142 11.85
## 2143 11.85
## 2144 15.09
## 2145 52.69
## 2146 18.00
## 2147 18.61
## 2148 18.61
## 2149 15.10
## 2150 18.63
## 2151 14.40
## 2152 17.09
## 2153 15.10
## 2154 15.10
## 2155 14.10
## 2156 7.78
## 2157 15.10
## 2158 21.05
## 2159 9.92
## 2160 14.10
## 2161 14.96
## 2162 14.06
## 2163 15.21
## 2164 19.10
## 2165 14.24
## 2166 11.85
## 2167 30.11
## 2168 13.65
## 2169 13.65
## 2170 15.63
## 2171 17.73
## 2172 15.23
## 2173 18.16
## 2174 18.16
## 2175 20.99
## 2176 18.37
## 2177 21.69
## 2178 12.18
## 2179 17.60
## 2180 13.08
## 2181 15.67
## 2182 15.67
## 2183 7.87
## 2184 41.02
## 2185 41.02
## 2186 41.02
## 2187 17.31
## 2188 15.10
## 2189 88.02
## 2190 9.34
## 2191 27.45
## 2192 66.98
## 2193 42.49
## 2194 42.49
## 2195 12.79
## 2196 15.38
## 2197 41.77
## 2198 69.57
## 2199 42.76
## 2200 18.44
## 2201 21.98
## 2202 8.11
## 2203 12.80
## 2204 15.74
## 2205 23.22
## 2206 21.89
## 2207 9.27
## 2208 42.76
## 2209 7.64
## 2210 8.03
## 2211 8.03
## 2212 16.46
## 2213 19.00
## 2214 13.15
## 2215 12.97
## 2216 18.28
## 2217 18.09
## 2218 23.35
## 2219 45.65
## 2220 12.13
## 2221 13.11
## 2222 18.32
## 2223 7.39
## 2224 44.80
## 2225 18.47
## 2226 7.92
## 2227 15.60
## 2228 13.97
## 2229 17.74
## 2230 7.39
## 2231 22.06
## 2232 15.56
## 2233 0.00
## 2234 58.76
## 2235 58.76
## 2236 8.88
## 2237 12.97
## 2238 14.10
## 2239 18.31
## 2240 15.27
## 2241 18.33
## 2242 28.11
## 2243 37.86
## 2244 16.81
## 2245 7.60
## 2246 14.10
## 2247 14.10
## 2248 27.49
## 2249 43.22
## 2250 19.45
## 2251 17.67
## 2252 18.11
## 2253 14.10
## 2254 18.00
## 2255 11.85
## 2256 22.74
## 2257 15.38
## 2258 16.08
## 2259 14.80
## 2260 15.10
## 2261 18.23
## 2262 9.34
## 2263 17.59
## 2264 10.55
## 2265 15.10
## 2266 8.72
## 2267 26.17
## 2268 16.11
## 2269 15.27
## 2270 17.42
## 2271 9.72
## 2272 9.72
## 2273 15.10
## 2274 15.10
## 2275 27.20
## 2276 9.26
## 2277 46.49
## 2278 12.98
## 2279 14.10
## 2280 18.40
## 2281 13.43
## 2282 13.43
## 2283 22.88
## 2284 8.01
## 2285 24.04
## 2286 24.04
## 2287 8.01
## 2288 14.41
## 2289 11.60
## 2290 9.94
## 2291 19.46
## 2292 7.78
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## 2294 34.42
## 2295 24.77
## 2296 18.85
## 2297 15.79
## 2298 54.70
## 2299 26.43
## 2300 26.43
## 2301 12.83
## 2302 14.10
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## 2305 18.70
## 2306 22.85
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## 2309 7.50
## 2310 7.39
## 2311 7.39
## 2312 7.39
## 2313 43.22
## 2314 15.60
## 2315 21.74
## 2316 11.85
## 2317 14.10
## 2318 22.19
## 2319 11.12
## 2320 18.23
## 2321 24.03
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## 2323 12.40
## 2324 12.40
## 2325 24.07
## 2326 13.11
## 2327 11.85
## 2328 13.21
## 2329 17.13
## 2330 26.37
## 2331 21.22
## 2332 18.16
## 2333 21.19
## 2334 15.83
## 2335 17.06
## 2336 17.06
## 2337 16.26
## 2338 15.85
## 2339 18.52
## 2340 18.33
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## 2342 18.89
## 2343 18.90
## 2344 15.11
## 2345 14.10
## 2346 37.28
## 2347 9.61
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## 2351 84.63
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## 2365 77.54
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## 2369 15.22
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## 2372 15.22
## 2373 15.22
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## 2375 11.74
## 2376 11.73
## 2377 15.23
## 2378 25.19
## 2379 17.09
## 2380 20.02
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## 2384 13.88
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## 2387 9.34
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## 2390 19.46
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## 2393 18.91
## 2394 23.28
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## 2396 7.41
## 2397 17.47
## 2398 11.76
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## 2400 15.57
## 2401 7.78
## 2402 20.84
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## 2404 16.19
## 2405 15.11
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## 2408 40.69
## 2409 12.74
## 2410 27.55
## 2411 14.10
## 2412 26.40
## 2413 15.65
## 2414 47.90
## 2415 47.90
## 2416 47.90
## 2417 47.90
## 2418 47.90
## 2419 25.63
## 2420 15.15
## 2421 18.74
## 2422 16.70
## 2423 16.70
## 2424 21.39
## 2425 15.10
## 2426 17.80
## 2427 18.63
## 2428 17.77
## 2429 17.04
## 2430 43.34
## 2431 13.37
## 2432 22.03
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## 2434 35.34
## 2435 23.60
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## 2437 12.51
## 2438 7.80
## 2439 21.15
## 2440 18.09
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## 2442 13.50
## 2443 11.85
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## 2445 34.65
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## 2450 15.65
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## 2452 15.65
## 2453 21.88
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## 2460 25.96
## 2461 7.55
## 2462 7.55
## 2463 20.10
## 2464 15.23
## 2465 11.22
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## 2467 16.11
## 2468 16.11
## 2469 25.05
## 2470 23.31
## 2471 16.79
## 2472 39.83
## 2473 19.77
## 2474 23.35
## 2475 37.31
## 2476 37.31
## 2477 7.50
## 2478 7.50
## 2479 7.50
## 2480 7.50
## 2481 23.31
## 2482 174.49
## 2483 68.74
## 2484 27.57
## 2485 27.57
## 2486 88.32
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## 2489 13.72
## 2490 26.47
## 2491 17.09
## 2492 14.52
## 2493 11.85
## 2494 13.45
## 2495 17.62
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## 2498 22.87
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## 2500 17.85
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## 2502 53.88
## 2503 25.29
## 2504 25.29
## 2505 31.78
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## 2508 17.16
## 2509 26.72
## 2510 35.53
## 2511 14.03
## 2512 18.45
## 2513 18.45
## 2514 18.66
## 2515 17.88
## 2516 24.05
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## 2518 17.59
## 2519 18.00
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## 2522 16.51
## 2523 12.99
## 2524 20.80
## 2525 20.80
## 2526 18.09
## 2527 15.30
## 2528 33.00
## 2529 16.83
## 2530 16.32
## 2531 22.67
## 2532 22.67
## 2533 14.66
## 2534 11.85
## 2535 15.73
## 2536 14.17
## 2537 15.10
## 2538 26.43
## 2539 18.31
## 2540 16.92
## 2541 12.75
## 2542 48.19
## 2543 17.36
## 2544 14.10
## 2545 15.85
## 2546 16.95
## 2547 30.90
## 2548 23.27
## 2549 7.53
## 2550 15.79
## 2551 19.53
## 2552 24.52
## 2553 18.23
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## 2555 17.59
## 2556 17.59
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## 2558 21.96
## 2559 8.23
## 2560 14.52
## 2561 16.11
## 2562 125.05
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## 2570 21.50
## 2571 106.31
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## 2573 26.94
## 2574 26.94
## 2575 16.04
## 2576 21.15
## 2577 18.84
## 2578 18.00
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## 2580 8.55
## 2581 12.18
## 2582 21.36
## 2583 14.52
## 2584 40.37
## 2585 14.10
## 2586 16.11
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## 2588 22.74
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## 2590 13.81
## 2591 13.81
## 2592 14.52
## 2593 14.52
## 2594 14.52
## 2595 14.52
## 2596 14.52
## 2597 14.52
## 2598 16.40
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## 2604 7.78
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## 2609 21.42
## 2610 21.42
## 2611 17.60
## 2612 23.92
## 2613 9.69
## 2614 11.73
## 2615 16.91
## 2616 18.59
## 2617 14.79
## 2618 12.10
## 2619 55.67
## 2620 10.49
## 2621 10.49
## 2622 26.24
## 2623 23.68
## 2624 21.15
## 2625 23.85
## 2626 36.15
## 2627 13.55
## 2628 44.10
## 2629 12.92
## 2630 14.52
## 2631 17.67
## 2632 27.59
## 2633 14.16
## 2634 16.11
## 2635 16.11
## 2636 16.11
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## 2638 15.10
## 2639 15.10
## 2640 16.11
## 2641 16.11
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## 2648 14.31
## 2649 12.76
## 2650 14.51
## 2651 15.10
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## 2655 9.72
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## 2659 19.35
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## 2661 50.85
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## 2669 12.13
## 2670 48.53
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## 2673 12.52
## 2674 28.14
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## 2678 15.10
## 2679 119.56
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## 2689 18.45
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## 2692 27.35
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## 2710 38.46
## 2711 11.73
## 2712 11.73
## 2713 11.73
## 2714 11.73
## 2715 0.00
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## 2718 16.26
## 2719 11.71
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## 2721 18.38
## 2722 15.23
## 2723 12.33
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## 2728 17.97
## 2729 19.14
## 2730 16.68
## 2731 19.70
## 2732 18.47
## 2733 12.79
## 2734 42.38
## 2735 21.50
## 2736 15.04
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## 2738 21.58
## 2739 21.58
## 2740 186.38
## 2741 28.53
## 2742 13.28
## 2743 63.11
## 2744 12.52
## 2745 33.29
## 2746 18.00
## 2747 15.10
## 2748 13.71
## 2749 22.29
## 2750 17.13
## 2751 20.98
## 2752 24.35
## 2753 8.23
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## 2755 10.66
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## 2760 28.89
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## 2763 16.33
## 2764 17.75
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## 2766 18.51
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## 2768 18.30
## 2769 13.55
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## 2771 19.65
## 2772 19.53
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## 2787 20.56
## 2788 20.56
## 2789 20.56
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## 2799 15.03
## 2800 14.10
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## 2804 24.38
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## 2808 11.97
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## 2810 7.78
## 2811 18.29
## 2812 36.76
## 2813 12.77
## 2814 18.95
## 2815 14.44
## 2816 15.10
## 2817 35.19
## 2818 18.95
## 2819 17.98
## 2820 17.98
## 2821 17.98
## 2822 22.07
## 2823 15.10
## 2824 13.23
## 2825 10.21
## 2826 13.08
## 2827 12.52
## 2828 46.92
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## 2830 18.26
## 2831 7.53
## 2832 107.87
## 2833 11.85
## 2834 19.83
## 2835 15.62
## 2836 11.85
## 2837 7.39
## 2838 16.84
## 2839 11.24
## 2840 11.73
## 2841 13.37
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## 2843 18.30
## 2844 15.56
## 2845 15.56
## 2846 16.05
## 2847 43.74
## 2848 14.80
## 2849 55.93
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## 2851 18.61
## 2852 19.40
## 2853 54.19
## 2854 11.85
## 2855 15.28
## 2856 18.59
## 2857 21.15
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## 2859 14.10
## 2860 7.41
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## 2862 15.17
## 2863 13.45
## 2864 18.89
## 2865 49.28
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## 2867 25.21
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## 2869 15.10
## 2870 15.10
## 2871 19.71
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## 2873 28.89
## 2874 15.10
## 2875 17.02
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## 2877 26.27
## 2878 22.88
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## 14111 7.39
## 14112 14.10
## 14113 25.81
## 14114 12.79
## 14115 15.35
## 14116 30.71
## 14117 26.26
## 14118 12.13
## 14119 14.10
## 14120 14.54
## 14121 31.14
## 14122 15.57
## 14123 24.60
## 14124 24.60
## 14125 13.65
## 14126 29.17
## 14127 35.63
## 14128 17.67
## 14129 54.37
## 14130 13.47
## 14131 13.37
## 14132 26.78
## 14133 17.54
## 14134 17.54
## 14135 13.51
## 14136 11.72
## 14137 11.72
## 14138 11.42
## 14139 17.85
## 14140 18.38
## 14141 15.41
## 14142 23.77
## 14143 78.20
## 14144 15.23
## 14145 13.98
## 14146 37.30
## 14147 26.29
## 14148 18.46
## 14149 8.96
## 14150 19.23
## 14151 34.15
## 14152 15.35
## 14153 25.26
## 14154 7.78
## 14155 9.45
## 14156 15.16
## 14157 16.49
## 14158 16.25
## 14159 34.69
## 14160 11.85
## 14161 30.26
## 14162 63.47
## 14163 12.25
## 14164 15.29
## 14165 57.08
## 14166 21.09
## 14167 16.92
## 14168 17.85
## 14169 17.85
## 14170 12.79
## 14171 18.27
## 14172 18.27
## 14173 17.67
## 14174 17.67
## 14175 17.67
## 14176 17.67
## 14177 17.67
## 14178 8.29
## 14179 16.70
## 14180 15.86
## 14181 12.14
## 14182 15.10
## 14183 8.72
## 14184 42.47
## 14185 18.52
## 14186 15.30
## 14187 38.90
## 14188 34.15
## 14189 38.46
## 14190 25.63
## 14191 14.10
## 14192 17.80
## 14193 11.85
## 14194 15.10
## 14195 34.20
## 14196 13.34
## 14197 16.86
## 14198 16.18
## 14199 16.18
## 14200 16.22
## 14201 12.09
## 14202 14.72
## 14203 17.60
## 14204 17.60
## 14205 17.60
## 14206 17.60
## 14207 14.52
## 14208 14.52
## 14209 14.52
## 14210 14.52
## 14211 7.56
## 14212 8.72
## 14213 8.96
## 14214 8.72
## 14215 18.00
## 14216 20.66
## 14217 15.31
## 14218 11.15
## 14219 17.60
## 14220 38.84
## 14221 16.11
## 14222 37.04
## 14223 37.04
## 14224 15.10
## 14225 17.83
## 14226 16.36
## 14227 15.27
## 14228 8.72
## 14229 16.92
## 14230 16.57
## 14231 23.30
## 14232 23.30
## 14233 13.87
## 14234 19.35
## 14235 13.25
## 14236 16.84
## 14237 7.78
## 14238 14.09
## 14239 14.09
## 14240 13.92
## 14241 58.08
## 14242 9.94
## 14243 18.23
## 14244 26.45
## 14245 16.24
## 14246 16.24
## 14247 16.24
## 14248 16.99
## 14249 15.23
## 14250 19.55
## 14251 15.10
## 14252 18.00
## 14253 12.48
## 14254 7.71
## 14255 18.23
## 14256 23.04
## 14257 16.68
## 14258 16.68
## 14259 15.20
## 14260 14.44
## 14261 15.38
## 14262 8.27
## 14263 11.75
## 14264 18.07
## 14265 11.85
## 14266 16.59
## 14267 7.39
## 14268 27.49
## 14269 44.97
## 14270 24.91
## 14271 25.63
## 14272 18.61
## 14273 66.07
## 14274 7.39
## 14275 11.16
## 14276 11.16
## 14277 69.41
## 14278 7.78
## 14279 15.79
## 14280 23.85
## 14281 17.00
## 14282 10.96
## 14283 12.48
## 14284 15.77
## 14285 16.95
## [ reached 'max' / getOption("max.print") -- omitted 98365 rows ]df_order_items %>%
group_by(product_id) %>%
summarise(total_sales = sum(price)) %>%
ungroup() %>%
arrange(desc(total_sales)) %>%
mutate(percent_total = round((total_sales/sum(total_sales) * 100),2)) %>%
left_join(df_products_translate %>%
select(product_id, product_category_name_english),
by = "product_id") ## # A tibble: 32,951 x 4
## product_id total_sales percent_total product_category_name_english
## <chr> <dbl> <dbl> <chr>
## 1 7654686c 63885 0.47 health_beauty
## 2 809f1595 54730. 0.4 health_beauty
## 3 e30376af 48899. 0.36 computers
## 4 c61f2ac4 47215. 0.35 computers_accessories
## 5 339b6058 43026. 0.32 bed_bath_table
## 6 c1e95ad7 41083. 0.3 computers_accessories
## 7 31f922db 38907. 0.290 baby
## 8 eb05bdc9 37734. 0.28 cool_stuff
## 9 d6772e08 37683. 0.28 watches_gifts
## 10 314663af 37609. 0.28 furniture_decor
## # ... with 32,941 more rowsState - Raw
We wil now see what states spend the most.
df_total_sales_states <- df_order_pay %>%
left_join(df_orders, "order_id") %>%
left_join(df_cust, "customer_id") %>%
select(payment_value, customer_state) %>%
group_by(customer_state) %>%
summarise(total_sales = sum(payment_value)) %>%
arrange(desc(total_sales)) %>%
mutate(total_sales_percent = round((total_sales/sum(total_sales) * 100),1))
df_total_sales_states## # A tibble: 27 x 3
## customer_state total_sales total_sales_percent
## <chr> <dbl> <dbl>
## 1 SP 5998227. 37.5
## 2 RJ 2144380. 13.4
## 3 MG 1872257. 11.7
## 4 RS 890899. 5.6
## 5 PR 811156. 5.1
## 6 SC 623086. 3.9
## 7 BA 616646. 3.9
## 8 DF 355141. 2.2
## 9 GO 350092. 2.2
## 10 ES 325968. 2
## # ... with 17 more rowsState - Population Adjusted
I now want to see what state spends the most per person.
State Population projections from the Brazilian Institute of Geography and Statistics were found here https://www.ibge.gov.br/en/statistics/social/population/18448-estimates-of-resident-population-for-municipalities-and-federation-units.html?=&t=resultados.
I will use these projections to calculate sales per population. First, I need to translate Brazil’s population data from to the state level. The population data imported was documented at the municipal district level.
df_brazil_pop_state <- df_brazil_pop %>%
group_by(uf) %>%
summarise(pop_estimate = sum(populacao_estimada))Now we join the state population data to the state sales data. Then we calculate money spent per 1K people.
df_total_sales_states %>%
left_join(df_brazil_pop_state, by = c("customer_state" = "uf")) %>%
mutate(sales_per_k = round(1000*total_sales / pop_estimate,1)) %>%
arrange(desc(sales_per_k))## # A tibble: 27 x 5
## customer_state total_sales total_sales_percent pop_estimate sales_per_k
## <chr> <dbl> <dbl> <int> <dbl>
## 1 SP 5998227. 37.5 45919049 131.
## 2 RJ 2144380. 13.4 17264943 124.
## 3 DF 355141. 2.2 3015268 118.
## 4 MG 1872257. 11.7 21168791 88.4
## 5 SC 623086. 3.9 7164788 87
## 6 ES 325968. 2 4018650 81.1
## 7 RS 890899. 5.6 11377239 78.3
## 8 PR 811156. 5.1 11433957 70.9
## 9 MT 187029. 1.2 3484466 53.7
## 10 GO 350092. 2.2 7018354 49.9
## # ... with 17 more rowsNothing generally unexpected here. At first glance, the more prosperous states spend more per person. Note, DF is the Federal District aka Brasilia. So while small (2.2% total sales), it is the capital.
To DO
- Customer Dataset - Convert Zip Code to String (add leading zeros)
- Cluster Analysis
- Heat Map overlay
For Later
Now we can calculate the number of days between times. The datetime values we have all contain the exact time of day an action occured. But we don’t want to deal with partial days. That is not how the customer would think about it. We only care about the day the action occurred, and the number of days in between.
df_orders_dt_1 <- df_orders_dt %>%
mutate(days_order_to_delivery = difftime(as_date(order_delivered_customer_date),
as_date(order_purchase_timestamp),
units = "days"))
df_orders_dt_1 %>%
ggplot(aes(x = days_order_to_delivery)) +
geom_histogram()## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 2965 rows containing non-finite values (stat_bin).